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15 Ways to Reduce Sample Size In Clinical Trials

How do you reduce sample size in clinical trials? Reducing sample size without losing power can be accomplished in one of three principles. This paper examines these methods and demonstrates 15 ways to reduce sample size in clinical trials.

15 Ways To Reduce Sample Size In Clinical Trials

The power of a statistical test is the probability that a test will reject the null hypothesis when the null hypothesis is false. That is, power reflects the probability of not committing a type II error. The two major factors affecting the power of a study are the sample size and the effect size.

Small Vs Large Sample SizesSample size is important for economic and ethical reasons. A study that has a sample size which is too small may produce inconclusive results and could also be considered unethical, because exposing human subjects or lab animals to the possible risks associated with research is only justifiable if there is a realistic chance that the study will yield useful information.

Similarly, a study that has a sample size which is too large will waste scarce resources and could expose more participants than necessary to any related risk. Thus an appropriate determination of the sample size used in a study is a crucial step in the design of a study.

How to reduce sample size without losing power in clinical trials?

Reducing sample size without losing power can be accomplished in one of three principles.

Improve the signal-to-noise ratio. To do this, you can either reduce the noise, strengthen the signal, or reduce variability (which will both reduce the noise and strengthen the signal).

Use a better statistical technique. By doing this you may be able to extract more information out of your data.

Multiplex. On a basic level this involves using the the same patient more than once.

Let us further examine the 15 waysto reduce sample size in clinical trials

1. Adjust for Independent Variables in the Final Analysis

An alternative to stratification is prespecified adjustment of the final analysis for imbalances. For example, you can prespecify in an MI trial that if one group has more anterior MIs than the other that adjustment to account for the imbalance will be made (the mortality rate for the group with more anterior MI will be adjusted downward for example).

This can reduce variability and sample size. This technique has all the typical shortcoming associated with multivariate analysis and I am not a proponent of it.

Potential reduction in sample size for clinical trial: 0 – 10%

2. Stratify the Patients

Similar to the above strategy, you can stratify the patients. This insures you minimize any potential baseline imbalance, and you can adjust your analysis to maximize the power of the study. Stratification is particularly helpful if the patient population is heterogeneous and the heterogeneity may impact the outcome significantly.

Potential reduction in sample size for clinical trial: 0 – 20%

3. Enrich the Patients

You can enrich the patient population in ways that will reduce the sample size substantial.

The first way is to make the patient population homogeneous. By making the patient population as similar to each other as possible, you will reduce the variability. For example, rather than including all patients with MIs, if you only include patients with anterior MIs, you are likely to have lower variability in outcomes. The tradeoff is that the generalizability of the study suffers. Alternatively, a compromise between power and generalizability would be to enroll all comers, but prespecify the primary endpoint as the enriched subgroup, and use either a secondary or hierarchical primary endpoint for the all-comers group.

The second is to select the patient population that is most likely to show a response or is more likely to show a greater amount of response. For example, if you were performing a pain study, patients with average pain score of 5 might be more likely to have 3 point decrease in pain than patients with average pain score of 3. Or patients who have had pain for a few week may be more likely to respond than patients with refractory pain who have had the symptoms for years.

The third is to select patients who are more likely to have more events. For example, patients with anterior MIs from the example above are more likely to die than patients with inferior MIs. If your endpoint is death, then you will have more power with anterior MI patients because there will be more events.

Potential reduction in sample size for clinical trial: 0 – 20%

4. Use Sustained Response

In some diseases, such as Crohn’s disease, the natural course of the disease is highly variable and/or the measurement of outcome is inconsistent. Many patients may have falsely positive responses briefly only to relapse. In that case, sustained response can remove some of the noise. A sustain response requires that the patient show improvement on multiple visits or over a certain minimal length of time.

Potential reduction in sample size for clinical trial: 0 – 25%

5. Use Pairwise Comparisons

If you can use the same patient multiple times, that will reduce the variability of the measurements and increase power. For example, rather than using average baseline blood pressure vs. average post-treatment blood pressure, use average change in blood pressure for each person.

Potential reduction in sample size for clinical trial: 0 – 30%

To view the complete detailed list of 1-15, click on the link below and quickly complete the form to read the 15 Ways To Reduce Sample Size In Clinical Trials.

I am a senior biostatistician and consultant for Tuberculosis clinical trials. My division is working on four area drug, diagnostic, prophylaxis and implementation research on Tuberculosis to bring new innovative diagnostic and treatment to reduce incidence of TB in India. Infact we intend to eliminate TB as per Govt Program. For that we are to plan 10 trials and i have to rovide the sample size which is small and precise as well.

Urban N Haankuku

Sample size in Bayesian frame work is not an issue. Since you have been working on a number of clinical trials all you need to work on closely is your prior distribution of your likelihood function and you will get your accurate results with small sample size

Liam from Statsols

Hi Urban. Thanks for commenting and sharing your opinion. Here is a response from our statistics team: "Bayesian analysis ameliorates the need for sample size determination to some extent and one advantage of the Bayesian approach is often higher efficiency (assuming proper informative prior specification). However, as more "standardised" Bayesian methods have become more popular (credible intervals, Bayes factors etc.) there has been increasing interest in developing sample size methods for targeting particular values of these parameters in a study as part of the study planning process. I do not wish to speak on the relative merits of these approaches but just to mention that these are trends we see in the literature at Statsols.
Additionally, Bayesian thinking is being used to supplement traditional frequentist sample size determination. For example, the concept for assurance has gained increasing interest by supplementing traditional (more ad-hoc) sensitivity analysis for sample size with a specification of prior distributions for parameters and getting a "probability of success" by taking account of this variability in the point estimates traditionally used for the power analysis."

Bhavya garg

Hi.. I need a case study which explains how we determine sample size in clinical trial and an example for hte functioning of sample size in clinical trial